AD omnivoice
All-in-one voice identity toolkit: speaker identification, voice library management, voice cloning, and speech-to-text. The only OpenClaw skill with speaker identification — recognize WHO is speaking, not just WHAT they said. 10 operations: identify speakers, manage a voice library (CRUD), clone voices, transcribe audio, voice swap, and persona voice replies. Activate when user sends voice/audio, asks to identify a speaker, manage a voice library, clone someone's voice, transcribe audio, or wants voice-based Q&A in a specific person's voice. Triggers: voice, audio, transcribe, 转文字, 语音, identify speaker, who is speaking, 这是谁的声音, 声纹识别, voice clone, 克隆声音, 模仿声音, voice library, 声音库, voice swap, 声音换皮.
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 43/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1074 tokens
- low The response is described with custom markup (3 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 704: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.